Strasmore Research
Market recaps wey dey break am down Matt ConnorBy Matt Connor · Updated 2026-08-02

Market Recap July 29 2026: Di Day Numbers

July 29, 2026 tape recap: index scoreboard, breadth, sector spread, options flow, quote tape, rates and calendar, with every figure query-backed.

Dis market recap for Wednesday, July 29, 2026 read the whole session from stored queries: SPY close-over-close change na -1.52%, the liquid tape advancer share na 27.2%, and options tape print 66.84 million contracts. Every window for below get explicit dates for both ends, so if dem run any panel SQL again, e go return these same figures.

Di scoreboard

Every change dey compare July 29 last regular-session minute bar with Tuesday July 28 own, for consecutive trading sessions. Rows dey follow alphabetical order, so every ETF dey keep one fixed position.

QuerySPY / QQQ / DIA / IWM: July 29 versus July 28 close, regular hours
The exact SQL behind every number
WITH prior AS (
    SELECT ticker, argMax(close, window_start) AS prior_close
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'DIA', 'IWM')
      AND window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00'
    GROUP BY ticker
),
sess AS (
    SELECT ticker,
           argMin(open, window_start) AS day_open,
           argMax(close, window_start) AS day_close,
           max(high) AS day_high,
           min(low) AS day_low,
           round(toFloat64(sum(volume)) / 1e6, 1) AS shares_traded_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'DIA', 'IWM')
      AND window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00'
    GROUP BY ticker
)
SELECT
    s.ticker AS ticker,
    round(toFloat64(p.prior_close), 2) AS prior_close,
    round(toFloat64(s.day_open), 2) AS day_open,
    round(toFloat64(s.day_close), 2) AS day_close,
    round((toFloat64(s.day_open) / toFloat64(p.prior_close) - 1) * 100, 2) AS gap_pct,
    round((toFloat64(s.day_close) / toFloat64(s.day_open) - 1) * 100, 2) AS intraday_pct,
    round((toFloat64(s.day_close) / toFloat64(p.prior_close) - 1) * 100, 2) AS pct_change,
    round(toFloat64(s.day_high), 2) AS day_high,
    round(toFloat64(s.day_low), 2) AS day_low,
    s.shares_traded_m
FROM sess s LEFT JOIN prior p ON s.ticker = p.ticker
ORDER BY ticker
Run this yourself

DIA move -2.19%, IWM -1.63%, QQQ -2.07%, and SPY -1.52% reach $729.51 close. Every row dey split the move into two legs: SPY open -0.11% from Tuesday close and move -1.41% from open to close. Overnight leg and intraday leg no need agree, and how dem split between the two na the session first fingerprint.

E day dey unusual?

One session number no mean much if we no see how e compare with the other days. So we rank the day inside im own trailing month with the same method.

QuerySPY day move for trailing context (open-to-close, June 29 reach July 29)
The exact SQL behind every number
SELECT
    round(anyIf(oc_pct, d = toDate('2026-07-29')), 2) AS spy_open_to_close_pct,
    arrayCount(x -> x > abs(anyIf(oc_pct, d = toDate('2026-07-29'))), groupArrayIf(abs(oc_pct), d != toDate('2026-07-29'))) + 1 AS spy_abs_move_rank,
    count() AS spy_sessions_compared,
    toString(min(d)) AS first_session
FROM (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           (argMax(toFloat64(close), window_start) / argMin(toFloat64(open), window_start) - 1) * 100 AS oc_pct
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY'
      AND window_start >= toDateTime('2026-06-29 13:30:00')
      AND window_start < toDateTime('2026-07-30 00:00:00')
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
    GROUP BY d
)
Run this yourself

SPY open-to-close move of -1.41% rank 1 out of 22 trailing sessions by absolute size, for window wey reach back to 2026-06-29. The rank count how many other sessions for the window move pass am, plus one. So first place mean say na the biggest move for the trailing month.

Breadth

Index level na one number. Breadth dey count how many stocks move along with am.

QueryLiquid-tape breadth: July 29 close versus July 28 close, $1M-traded filter
The exact SQL behind every number
SELECT
    countIf(c29 > c28 AND liquid) AS advancers,
    countIf(c29 < c28 AND liquid) AS decliners,
    countIf(c29 = c28 AND liquid) AS unchanged,
    countIf(liquid) AS liquid_tickers,
    countIf(NOT liquid) AS dropped_by_liquidity_filter,
    round(100.0 * countIf(c29 > c28 AND liquid) / countIf(liquid), 1) AS advancer_pct
FROM (
    SELECT ticker, c28, c29, dv29 >= 1000000 AS liquid
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00') AS c28,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00') AS c29,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-29 13:30:00') AS dv29
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-29 20:00:00'
        GROUP BY ticker
        HAVING c28 > 0 AND c29 > 0
    )
)
Run this yourself

Among 6066 names wey pass one-million-dollar regular-hours turnover bar, 1652 close above Tuesday close and 4342 close below, making advancer share be 27.2%. The filter leave out 5363 thinner names, but dem still count am here instead of quietly throwing dem away.

Di mega-cap shelf

Na every session, na di same eight mega-cap names dey show for here. Dem arrange am alphabetically so each one get im own row. Di fixed basket na di main point: reader go sabi di rows, and no editor go choose winners after di fact.

QueryEight mega-caps: change versus July 28 and regular-hours dollars, July 29
The exact SQL behind every number
WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-29 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-29 00:00:00')) AS day_close,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-29 00:00:00') / 1e9, 2) AS day_dollar_bn
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AAPL', 'AMZN', 'AVGO', 'GOOGL', 'META', 'MSFT', 'NVDA', 'TSLA')
      AND ((window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00')
        OR (window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00'))
    GROUP BY ticker
)
SELECT
    ticker,
    round(prior_close, 2) AS prior_close,
    round(day_close, 2) AS day_close,
    round((day_close / prior_close - 1) * 100, 2) AS pct_chg,
    day_dollar_bn
FROM per_name
ORDER BY ticker
Run this yourself

AAPL moved -0.63%, META -1.12%, MSFT -0.27%, and NVDA -3.51% for 22.26 billion dollars regular-hours turnover, while TSLA dey at -2.97%. Di dollar column dey show how much of di tape these eight names carry by themselves. Di breadth panel for top na di check of how far di rest of di market move together with dem.

The shares wey move today

Both boards need five million dollars of regular-hours turnover. Dem no include any name wey e split execute between the two closes wey dem measure. Dem also no include one symbol wey dem reuse under the house ambiguity guard wey notes describe.

QueryBiggest gainers and decliners: July 29 close versus July 28 close, $5M+ traded, splits no dey count
The exact SQL behind every number
SELECT ticker, board, day_pct, day_dollar_m
FROM (
    SELECT 'gainers' AS board, ticker, round((c29 / c28 - 1) * 100, 1) AS day_pct, round(dv / 1e6, 1) AS day_dollar_m
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00') AS c28,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00') AS c29,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-29 13:30:00') AS dv
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker NOT IN ('SPCX')
          AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits WHERE execution_date > '2026-07-28' AND execution_date <= '2026-07-29')
          AND window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-29 20:00:00'
        GROUP BY ticker
        HAVING c28 > 0 AND c29 > 0 AND dv >= 5000000
    )
    ORDER BY day_pct DESC
    LIMIT 8
    UNION ALL
    SELECT 'decliners' AS board, ticker, round((c29 / c28 - 1) * 100, 1) AS day_pct, round(dv / 1e6, 1) AS day_dollar_m
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00') AS c28,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00') AS c29,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-29 13:30:00') AS dv
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker NOT IN ('SPCX')
          AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits WHERE execution_date > '2026-07-28' AND execution_date <= '2026-07-29')
          AND window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-29 20:00:00'
        GROUP BY ticker
        HAVING c28 > 0 AND c29 > 0 AND dv >= 5000000
    )
    ORDER BY day_pct ASC
    LIMIT 8
)
ORDER BY board DESC, abs(day_pct) DESC
Run this yourself

The biggest gainer for the board, DFNS, move 119.8% on 544.9 million dollars wey traders trade. The biggest decliner, YYAI, print -68.1% on 8.5 million. This page record the sizes and the receipts. E no attach any story to dem.

Sector dispersion

The eleven SPDR select-sector funds, July 29 close compared with July 28 close, ranked from best to worst. The basket don set and no vendor classification dey involved.

QuerySector ETFs, July 29 close versus July 28 close, ranked
The exact SQL behind every number
SELECT sector, day_pct, round(max(day_pct) OVER () - day_pct, 2) AS points_behind_best
FROM (
    SELECT multiIf(ticker = 'XLK', 'Technology', ticker = 'XLC', 'Communications', ticker = 'XLE', 'Energy',
                   ticker = 'XLF', 'Financials', ticker = 'XLI', 'Industrials', ticker = 'XLB', 'Materials',
                   ticker = 'XLP', 'Staples', ticker = 'XLRE', 'Real Estate', ticker = 'XLU', 'Utilities',
                   ticker = 'XLV', 'Health Care', 'Consumer Discretionary') AS sector,
           round((c29 / c28 - 1) * 100, 2) AS day_pct
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start < '2026-07-29 00:00:00') AS c28,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-29 00:00:00') AS c29
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
          AND ((window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00')
            OR (window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00'))
        GROUP BY ticker
        HAVING c28 > 0 AND c29 > 0
    )
)
ORDER BY day_pct DESC
Run this yourself

Energy lead the table at 1.89%, while Industrials dey bottom at -3.21%, 5.1 percentage points behind. That spread na the day’s sector dispersion: when all eleven land within one point, the market tape dey look very different from one wey spread across several points.

Wey dollars change hands

QueryTop 6 by dollars traded, top 4 by shares traded: July 29 regular hours
The exact SQL behind every number
SELECT leaderboard, ticker, dollar_volume_bn, shares_m,
    round(1000 * dollar_volume_bn / shares_m, 2) AS implied_avg_price
FROM (
    SELECT
        'by dollars traded' AS leaderboard,
        ticker,
        round(sum(toFloat64(close) * toFloat64(volume)) / 1e9, 2) AS dollar_volume_bn,
        round(sum(toFloat64(volume)) / 1e6, 1) AS shares_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00'
      AND ticker NOT IN ('SPCX')
    GROUP BY ticker
    ORDER BY dollar_volume_bn DESC
    LIMIT 6
    UNION ALL
    SELECT
        'by shares traded' AS leaderboard,
        ticker,
        round(sum(toFloat64(close) * toFloat64(volume)) / 1e9, 2) AS dollar_volume_bn,
        round(sum(toFloat64(volume)) / 1e6, 1) AS shares_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00'
      AND ticker NOT IN ('SPCX')
    GROUP BY ticker
    ORDER BY shares_m DESC
    LIMIT 4
)
ORDER BY leaderboard, if(leaderboard = 'by shares traded', shares_m, dollar_volume_bn) DESC
Run this yourself

MU lead the dollar board with 44.99 billion regular-hours turnover, while SPY follow am with 42.29 billion. The share board dey answer another question: SNXX top am with 173.4 million shares at an implied average price of $7.32. Dollar volume show where market attention dey, share volume show how much trading dey happen, and the per-name version of this measure na relative volume.

Di option tape

QueryOptions tape: contracts, call share, same-day share versus Tuesday, busiest SPY contract
The exact SQL behind every number
WITH
    (
        SELECT (strike, typ, vol_m, is_0dte)
        FROM (
            SELECT toFloat64(any(strike_price)) AS strike, any(option_type) AS typ,
                   round(toFloat64(sum(size)) / 1e6, 2) AS vol_m,
                   if(substring(ticker, length(ticker) - 14, 6) = '260729', 1, 0) AS is_0dte
            FROM global_markets.options_trades
            WHERE sip_timestamp >= '2026-07-29 00:00:00' AND sip_timestamp < '2026-07-30 00:00:00'
              AND underlying_symbol = 'SPY'
            GROUP BY ticker
            ORDER BY vol_m DESC, strike ASC
            LIMIT 1
        )
    ) AS top_spy,
    (
        SELECT round(toFloat64(argMax(close, window_start)), 2)
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY' AND window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00'
    ) AS spy_regular_close,
    (
        SELECT round(toFloat64(sum(size)) / 1e6, 2)
        FROM global_markets.options_trades
        WHERE sip_timestamp >= '2026-07-28 00:00:00' AND sip_timestamp < '2026-07-29 00:00:00'
    ) AS jul28_contracts_m,
    (
        SELECT round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260728') / sum(size), 1)
        FROM global_markets.options_trades
        WHERE sip_timestamp >= '2026-07-28 00:00:00' AND sip_timestamp < '2026-07-29 00:00:00'
    ) AS jul28_pct_0dte
SELECT
    round(count() / 1e6, 2) AS option_prints_m,
    round(toFloat64(sum(size)) / 1e6, 2) AS contracts_m,
    jul28_contracts_m,
    round(100.0 * sumIf(size, option_type = 'C') / sum(size), 1) AS call_pct_of_volume,
    round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260729') / sum(size), 1) AS pct_0dte,
    jul28_pct_0dte,
    spy_regular_close,
    top_spy.1 AS top_spy_strike,
    top_spy.2 AS top_spy_type,
    top_spy.3 AS top_spy_contracts_m,
    top_spy.4 AS top_spy_is_0dte,
    round(top_spy.1 - spy_regular_close, 2) AS top_spy_strike_minus_close
FROM global_markets.options_trades
WHERE sip_timestamp >= '2026-07-29 00:00:00' AND sip_timestamp < '2026-07-30 00:00:00'
Run this yourself

Di options tape print 11.03 million trades for 66.84 million contracts, beside Tuesday own 59.28 million. Calls carry 53.6% of contract volume. Contracts wey dey expire for that same session, di zero-days-to-expiry crowd, carry 33.1% compared with Tuesday own 29%. Expiration timing na wetin dey set di pattern. Di busiest SPY contract na di 735 P, with 0.36 million contracts. Its strike dey 5.49 dollars from SPY own $729.51 regular close, measured as strike minus close.

Quote tape

Quote data na the scarcest dataset for this desk, and dem dey measure am every session. Ordinary days sef dey enter record.

QueryStocks NBBO update count: July 29 versus July 28, with named-ticker updates (millions)
The exact SQL behind every number
SELECT
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-29')) / 1e6, 2) AS jul29_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-28')) / 1e6, 2) AS jul28_updates_m,
    round((countIf(toDate(sip_timestamp) = toDate('2026-07-29')) / countIf(toDate(sip_timestamp) = toDate('2026-07-28')) - 1) * 100, 1) AS day_over_day_pct,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-29') AND ticker = 'SPY') / 1e6, 2) AS jul29_spy_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-29') AND ticker = 'QQQ') / 1e6, 2) AS jul29_qqq_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-29') AND ticker = 'NVDA') / 1e6, 2) AS jul29_nvda_updates_m
FROM global_markets.cache_stocks_quotes
WHERE sip_timestamp >= '2026-07-28 00:00:00' AND sip_timestamp < '2026-07-30 00:00:00'
Run this yourself

Stock-quote tape carry 722.65 million NBBO updates against 536.07 million on Tuesday. Na day-over-day change of 34.8%. SPY record 7.32 million updates, QQQ 8.72 million, and NVDA 4.41 million.

QuerySeven names: RTH median quoted spread in basis points, with quote-quality counts, July 29
The exact SQL behind every number
SELECT ticker,
       round(quantileExactIf(0.5)(10000 * (toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2), bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price), 2) AS median_spread_bps,
       round(count() / 1e6, 2) AS quote_updates_m,
       countIf(bid_price <= 0 OR ask_price <= 0) AS one_sided_quote_count,
       countIf(bid_price > ask_price AND bid_price > 0 AND ask_price > 0) AS crossed_quote_count
FROM global_markets.cache_stocks_quotes
WHERE ticker IN ('AAPL', 'DIA', 'IWM', 'NVDA', 'QQQ', 'SPY', 'TSLA')
  AND sip_timestamp >= '2026-07-29 13:30:00' AND sip_timestamp < '2026-07-29 20:00:00'
GROUP BY ticker
HAVING countIf(bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price) > 0
ORDER BY ticker
Run this yourself

SPY regular-hours median quoted spread measure 0.27 basis points of mid. QQQ measure 0.75 and NVDA 1.55. The last two columns na the disclosure: dem count one-sided and crossed quotes for each name, then set dem aside from the median instead of quietly dropping dem. Crossed quote, where bid dey above ask, na normal artifact of consolidated feed wey dem stitch from many venues at nanosecond resolution.

QuerySPY median spread ranked against every July session, tightest first
The exact SQL behind every number
SELECT round(anyIf(spread_bps, d = toDate('2026-07-29')), 2) AS jul29_median_spread_bps,
       arrayCount(x -> x < anyIf(spread_bps, d = toDate('2026-07-29')), groupArrayIf(spread_bps, d != toDate('2026-07-29'))) + 1 AS rank_tightest,
       count() AS sessions_compared,
       toString(min(d)) AS first_session
FROM (
    SELECT toDate(sip_timestamp) AS d,
           quantileExactIf(0.5)(10000 * (toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2), bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price) AS spread_bps
    FROM global_markets.cache_stocks_quotes
    WHERE ticker = 'SPY'
      AND sip_timestamp >= '2026-07-01 13:30:00' AND sip_timestamp < '2026-07-29 20:00:00'
      AND (toHour(sip_timestamp) * 60 + toMinute(sip_timestamp)) BETWEEN 810 AND 1199
    GROUP BY d
    HAVING countIf(bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price) > 0
)
Run this yourself

When dem rank am against every July session with the same logic, that day SPY median spread of 0.27 basis points come 20 of 20, counting from the tightest, for window wey start 2026-07-01. For quiet tape, na this be the panel point: ordinary liquidity day na finding, and dem publish am with clear boundaries.

QueryOptions NBBO tape: total updates versus the stock tape, plus the SPY root slice, July 29
The exact SQL behind every number
WITH
    (SELECT count() FROM global_markets.cache_options_quotes WHERE sip_timestamp >= '2026-07-29 00:00:00' AND sip_timestamp < '2026-07-30 00:00:00') AS jul29_options_rows,
    (SELECT count() FROM global_markets.cache_stocks_quotes WHERE sip_timestamp >= '2026-07-29 00:00:00' AND sip_timestamp < '2026-07-30 00:00:00') AS jul29_stock_quote_rows
SELECT
    round(jul29_options_rows / 1e9, 2) AS jul29_options_bn,
    round(jul29_options_rows / jul29_stock_quote_rows, 1) AS options_to_stock_ratio,
    round((SELECT count() FROM global_markets.cache_options_quotes WHERE ticker >= 'O:SPY26' AND ticker < 'O:SPY27' AND sip_timestamp >= '2026-07-29 13:30:00' AND sip_timestamp < '2026-07-29 20:00:00') / 1e6, 0) AS jul29_spy_options_m
Run this yourself

Options NBBO tape run 13.55 billion updates, 18.8 times the stock quote tape. SPY root alone record 600 million regular-hours updates.

Rates

QueryTreasury curve prints wey dey on file, July 24 reach July 29
The exact SQL behind every number
SELECT toString(date) AS date,
       round(toFloat64(yield_2_year), 2) AS yield_2y_pct,
       round(toFloat64(yield_10_year), 2) AS yield_10y_pct,
       round(toFloat64(yield_30_year), 2) AS yield_30y_pct,
       round((toFloat64(yield_10_year) - toFloat64(yield_2_year)) * 100) AS spread_2s10s_bp
FROM global_markets.treasury_yields
WHERE date >= '2026-07-24' AND date <= '2026-07-29'
ORDER BY date
Run this yourself

Treasury file dey about one session behind the tape, so this panel dey show the prints wey e hold: 4 dated rows for the window. The latest one, dated 2026-07-29, put the two-year at 4.22%, the ten-year at 4.67% and the thirty-year at 5.2%, with two-to-ten-year spread of 45 basis points.

The calendar wey dey behind the day

QueryEx-dividends, splits, listings, news, and the July 29 SEC filing mix
The exact SQL behind every number
WITH
    (
        SELECT (argMax(t, (n, t)), max(n))
        FROM (
            SELECT t, count() AS n
            FROM (
                SELECT arrayJoin(tickers) AS t
                FROM global_markets.stocks_news
                WHERE published_utc >= '2026-07-29 04:00:00' AND published_utc < '2026-07-30 04:00:00'
            )
            WHERE t != 'SPCX'
            GROUP BY t
        )
    ) AS top_news,
    (
        SELECT (count(), uniqExact(cik), countIf(form_type = '4'), countIf(form_type = '8-K'), countIf(form_type = '424B2'), countIf(form_type = '10-Q'))
        FROM global_markets.stocks_sec_edgar_index
        WHERE filing_date = '2026-07-29'
    ) AS fil
SELECT
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-07-29') AS ex_dividend_records,
    (SELECT countIf(toFloat64(split_from) > toFloat64(split_to)) FROM global_markets.stocks_splits WHERE execution_date = '2026-07-29') AS reverse_splits,
    (SELECT countIf(toFloat64(split_to) > toFloat64(split_from)) FROM global_markets.stocks_splits WHERE execution_date = '2026-07-29') AS forward_splits,
    (SELECT count() FROM global_markets.stocks_ipos WHERE listing_date = '2026-07-29') AS listings,
    (SELECT count() FROM global_markets.stocks_news WHERE published_utc >= '2026-07-29 04:00:00' AND published_utc < '2026-07-30 04:00:00') AS news_articles,
    (SELECT uniqExact(JSONExtractString(publisher, 'name')) FROM global_markets.stocks_news WHERE published_utc >= '2026-07-29 04:00:00' AND published_utc < '2026-07-30 04:00:00') AS news_publishers,
    top_news.1 AS top_news_ticker,
    top_news.2 AS top_news_n,
    fil.1 AS fil_total,
    fil.2 AS fil_filers,
    fil.3 AS fil_form4,
    fil.4 AS fil_8k,
    fil.5 AS fil_424b2,
    fil.6 AS fil_10q
Run this yourself

85 dividend records went ex-dividend for July 29, 2 reverse and 1 forward splits execute, and 1 new listings enter market. The news feed carry 190 articles from 2 publishers, with NVDA as the ticker wey get the most coverage for this feed window, at 13 articles. The EDGAR daily index get 5742 filings for that date from 3064 different filers: 712 insider Form 4 reports, 440 8-K current reports, 884 424B2 pricing supplements and 174 10-Q quarterly reports. That index dey follow its own schedule, and this panel report wetin e hold when dem generate am.

Wetin dey come

For the rest of the week, read from the same tables, but make una deliberately look past the period.

QueryJuly 30 and 31 for calendar: closures, ex-dividends, splits, the Friday expiry, and the short-interest lag
The exact SQL behind every number
SELECT
    (SELECT count() FROM global_markets.stocks_market_holidays WHERE date >= '2026-07-30' AND date <= '2026-07-31' AND status != 'open') AS closures_rest_of_week,
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date >= '2026-07-30' AND ex_dividend_date <= '2026-07-31') AS exdiv_records_rest_of_week,
    (SELECT countIf(ticker IN ('AAPL', 'MSFT', 'JPM', 'KO', 'JNJ', 'XOM', 'CVX', 'PG', 'WMT', 'HD')) FROM global_markets.stocks_dividends WHERE ex_dividend_date >= '2026-07-30' AND ex_dividend_date <= '2026-07-31') AS household_exdivs,
    (SELECT count() FROM global_markets.stocks_splits WHERE execution_date >= '2026-07-30' AND execution_date <= '2026-07-31') AS splits_rest_of_week,
    round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260731') / sum(size), 1) AS jul31_expiry_pct_of_wed_volume,
    (SELECT toString(max(settlement_date)) FROM global_markets.stocks_short_interest WHERE settlement_date <= '2026-07-29') AS latest_short_interest_settlement
FROM global_markets.options_trades
WHERE sip_timestamp >= '2026-07-29 00:00:00' AND sip_timestamp < '2026-07-30 00:00:00'
Run this yourself

The holiday table show 0 closures across Thursday July 30 and Friday July 31. 611 dividend records go ex-dividend across those two sessions. 0 of them dey among the ten household names we check, and 12 splits dey scheduled to execute. From Wednesday option volume, 19.7% already dey inside contracts wey expire Friday, July 31. The newest short-interest settlement wey dey on file na 2026-07-15, and the file dey publish with enough lag to get its own explanation.

Session wey dem don verify

QuerySession verification: first/last SPY bar ET, regular-bar count, holiday receipts, next closure
The exact SQL behind every number
SELECT
    formatDateTime(min(toTimeZone(window_start, 'America/New_York')), '%H:%i') AS first_spy_bar_et,
    formatDateTime(max(toTimeZone(window_start, 'America/New_York')), '%H:%i') AS last_spy_bar_et,
    countIf(window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00') AS regular_session_bars,
    uniqExactIf(toDate(toTimeZone(window_start, 'America/New_York')), window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00') AS day_sessions,
    (SELECT count() FROM global_markets.stocks_market_holidays WHERE date = '2026-07-29') AS jul29_holiday_rows,
    (SELECT toString(min(date)) FROM global_markets.stocks_market_holidays WHERE date > '2026-07-29' AND date <= '2026-12-31' AND status = 'closed') AS next_closure_date,
    (SELECT argMin(name, date) FROM global_markets.stocks_market_holidays WHERE date > '2026-07-29' AND date <= '2026-12-31' AND status = 'closed') AS next_closure_name
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY' AND window_start >= '2026-07-29 00:00:00' AND window_start < '2026-07-30 00:00:00'
Run this yourself

Full regular session: first SPY bar na 04:00 ET, last one na 19:59 ET, 390 regular-hours bars, 1 session dey inside the window, and 0 holiday rows dey for the date. The next scheduled closure na Labor Day on 2026-09-07.

FAQ

How stock market perform for Wednesday, July 29, 2026?

SPY change -1.52% close over close reach $729.51, while QQQ dey -2.07%, DIA dey -2.19% and IWM dey -1.63%. Among liquid names, 1652 rise and 4342 fall.

Which sector lead the board on July 29, 2026?

Energy, at 1.89%, based on the eleven SPDR select-sector funds. Among the eleven, Industrials na the weakest, and e print -3.21%.

How busy options market be on July 29, 2026?

66.84 million contracts trade, compared with 59.28 million for the previous session. Same-day contracts make up 33.1% of volume, while calls make up 53.6%.

Which stock trade the highest dollar volume on July 29, 2026?

MU, with 44.99 billion regular-hours dollar volume, ahead of SPY with 42.29 billion.

Data notes

This edition dey continue the daily series. The previous daily edition na July 10, 2026, while the weekly recap carry the week before this one. Dem arrange the named per-ticker panels alphabetically, so prose references dey point to fixed rows. Dem arrange leaderboards and mover boards by value, and every position claim wey dem carry get encoded as a sanity bound. The mega-cap basket and the eleven-fund sector basket na fixed sets wey dem declare, no be vendor classifications. The mover boards use a five-million-dollar regular-hours turnover bar. Dem exclude any name wey split execute between the two closes wey dem dey measure. Dem also exclude one reused symbol under the house ambiguity guard, so every callout go always point to a name wey person fit verify. The quote panels count one-sided and crossed quotes for each name instead of silently dropping them. Treasury's file and the EDGAR daily index dey arrive on their own schedules, so those panels report wetin dem get instead of assuming say the data don arrive. No implied-volatility index dey here. Those series no get license for this warehouse, so dem read volatility from the tape through ranges, same-day options share and quote behavior.

Methodology

  • Market data source: consolidated tape. delayed_stocks_minute_aggs na for prices and volumes, options_trades na for the options tape, cache_stocks_quotes and cache_options_quotes na for the NBBO panels.
  • Close: na the last regular-session minute bar, never be assumed 16:00 print and never be extended-hours print.
  • Time zone handling: all stored timestamps dey UTC; WHERE clauses dey use raw UTC literals, and toTimeZone dey show only for SELECT lists as ET labels.
  • Session verification: na from the holiday table plus observed bars, never be assumed from the calendar.
  • Prior-session comparisons: query dey calculate am from July 28, never carry am over from previous post.
  • Decimals: price, size and volume columns dey cast to Float64 before any division or product.
  • Deterministic aggregates: exact quantiles and tuple-keyed tie-breaks dey apply throughout; every ordering or sign claim for the prose get sanity bound encoded.
  • Warehouse as-of date: August 1, 2026, three days after the session, don pass the tape normal one-to-two-day ingest lag; the bounded receipts above go hold the post if any dataset no dey available when dem generate am.

Cross-links: the previous daily recap, the weekly recap, when options expire, what a bid-ask spread is, and the two-to-ten-year spread.

Every query above dey run unchanged for the Strasmore terminal if you wan point one window to another session.

#recap#daily#spy#qqq#market structure